Filling the binary images of draped fabric with pix2pix convolutional neural network. (May 2020)

• Record Type: Journal Article
• Authors: Yu, Zhicai; Zhong, Yueqi; Gong, R Hugh; Xie, Haoyang
• Is Part Of: Journal of engineered fibers and fabrics. Volume 15(2020)
• Publication Date: 2020-05
• Subjects: Draped fabric -- top view image -- binary image -- grayscale image -- filling images; Nonwoven fabrics -- Periodicals; Fibers -- Periodicals; Fibers; Nonwoven fabrics; Periodicals; 677.6
• DOI: 10.1177/1558925020921544
• Languages: English
• ISSNs: 1558-9250

Abstract

To fill the binary image of draped fabric into a comparable grayscale image with detailed shade information, the three-dimensional point cloud of draped fabric was obtained with a self-built three-dimensional scanning device. The three-dimensional point cloud of drape fabric is encapsulated into a triangular mesh, and the binary and grayscale images of draped fabric were rendered in virtual environments separately. A pix2pix convolutional neural network with the binary image of draped fabric as input and the grayscale image of draped fabric as output was constructed and trained. The relationship between the binary image and the grayscale image was established. The results show that the trained pix2pix neural network can fill unknown binary top view images of draped fabric to grayscale images. The average pixel cosine similarity between filling results and ground truth could reach 0.97.